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Record W4206566015 · doi:10.22215/etd/2021-14766

Community Peer Support Among Individuals Living with Spinal Cord Injury

2021· dissertation· en· W4206566015 on OpenAlexaff
Joy McLeod

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsLonelinessPeer supportSocial supportPsychologySpinal cord injuryPeer reviewLife satisfactionClinical psychologyGerontologyMedicinePsychiatrySocial psychologySpinal cord

Abstract

fetched live from OpenAlex

Previous research has demonstrated that the relation between peer support and well-being is unclear among individuals living with SCI.The present study expands on these findings by exploring the conditions under which peer support is more strongly associated with better adjustment.Participants were 135 individuals living with SCI recruited through social media and major SCI organizations globally who completed an online self-report questionnaire.Although peer support, as measured by the SCI-PSI, was not associated with better adjustment, a measure of perceived support (i.e., the level of satisfaction with the peer support one receives) was associated with all indicators of adjustment.Individuals who were more satisfied with the peer support they received exhibited fewer depressive symptoms, had higher subjective well-being, experienced less loneliness, and exhibited better community reintegration.Longitudinal research is needed to better understand the adjustment trajectory of persons with SCI who are recipients of peer support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.422
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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